10_ML_Simple Linear Regression-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b51996ce-73be-4c18-9d23-372a82d11ba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b91ef7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "----------------\n",
    "ML Pipeline\n",
    "----------------\n",
    "1. Read the data\n",
    "2. Prepare the data : EDA/Feature Engineering\n",
    "3. X, Y\n",
    "4. Train-test split\n",
    "5. Scaling\n",
    "6. Model training\n",
    "7. Model Evaluation\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30e5bb63-6554-41dd-b6bb-ac690cb349ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. read the data\n",
    "# Starts\n",
    "from sklearn.datasets import load_diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db64dcb6-8d58-42b3-b1de-f0c7a10c743d",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = load_diabetes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d67e2924-82d7-4d71-b8de-13a3879b3757",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "         -0.00422151,  0.00306441]]),\n",
       " 'target': array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
       "         69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
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       "         77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
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       "        220.,  57.]),\n",
       " 'frame': None,\n",
       " 'DESCR': '.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 442\\n\\n:Number of Attributes: First 10 columns are numeric predictive values\\n\\n:Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n:Attribute Information:\\n    - age     age in years\\n    - sex\\n    - bmi     body mass index\\n    - bp      average blood pressure\\n    - s1      tc, total serum cholesterol\\n    - s2      ldl, low-density lipoproteins\\n    - s3      hdl, high-density lipoproteins\\n    - s4      tch, total cholesterol / HDL\\n    - s5      ltg, possibly log of serum triglycerides level\\n    - s6      glu, blood sugar level\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times the square root of `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttps://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\\n',\n",
       " 'feature_names': ['age',\n",
       "  'sex',\n",
       "  'bmi',\n",
       "  'bp',\n",
       "  's1',\n",
       "  's2',\n",
       "  's3',\n",
       "  's4',\n",
       "  's5',\n",
       "  's6'],\n",
       " 'data_filename': 'diabetes_data_raw.csv.gz',\n",
       " 'target_filename': 'diabetes_target.csv.gz',\n",
       " 'data_module': 'sklearn.datasets.data'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes\n",
    "# This data set is in the form of key-value pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10ebf89d-d272-4f55-8c29-57893ab09789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename', 'data_module'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31cb1e1d-be9a-4912-aa07-b42732f4d28d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _diabetes_dataset:\n",
      "\n",
      "Diabetes dataset\n",
      "----------------\n",
      "\n",
      "Ten baseline variables, age, sex, body mass index, average blood\n",
      "pressure, and six blood serum measurements were obtained for each of n =\n",
      "442 diabetes patients, as well as the response of interest, a\n",
      "quantitative measure of disease progression one year after baseline.\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 442\n",
      "\n",
      ":Number of Attributes: First 10 columns are numeric predictive values\n",
      "\n",
      ":Target: Column 11 is a quantitative measure of disease progression one year after baseline\n",
      "\n",
      ":Attribute Information:\n",
      "    - age     age in years\n",
      "    - sex\n",
      "    - bmi     body mass index\n",
      "    - bp      average blood pressure\n",
      "    - s1      tc, total serum cholesterol\n",
      "    - s2      ldl, low-density lipoproteins\n",
      "    - s3      hdl, high-density lipoproteins\n",
      "    - s4      tch, total cholesterol / HDL\n",
      "    - s5      ltg, possibly log of serum triglycerides level\n",
      "    - s6      glu, blood sugar level\n",
      "\n",
      "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times the square root of `n_samples` (i.e. the sum of squares of each column totals 1).\n",
      "\n",
      "Source URL:\n",
      "https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n",
      "\n",
      "For more information see:\n",
      "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n",
      "(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(diabetes.DESCR)\n",
    "# description of diabetes data set\n",
    "\n",
    "\"\"\"\n",
    "Independent variables:\n",
    "    - age     age in years\n",
    "    - sex\n",
    "    - bmi     body mass index\n",
    "    - bp      average blood pressure\n",
    "    - s1      tc, total serum cholesterol\n",
    "    - s2      ldl, low-density lipoproteins\n",
    "    - s3      hdl, high-density lipoproteins\n",
    "    - s4      tch, total cholesterol / HDL\n",
    "    - s5      ltg, possibly log of serum triglycerides level\n",
    "    - s6      glu, blood sugar level\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "Target Variable (or) Response (or) Dependent Variable: \n",
    "    measure of disease progression one year after baseline\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "62681d75-3389-43ec-99bf-a9ab158d958d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# problem statement :\n",
    "# To predict disease progression one year after baseine using 10 features or 10 independent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "01bf57f9-35f2-4453-81f5-bd232241b3ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "         0.01990749, -0.01764613],\n",
       "       [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
       "        -0.06833155, -0.09220405],\n",
       "       [ 0.08529891,  0.05068012,  0.04445121, ..., -0.00259226,\n",
       "         0.00286131, -0.02593034],\n",
       "       ...,\n",
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       "        -0.04688253,  0.01549073],\n",
       "       [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
       "         0.04452873, -0.02593034],\n",
       "       [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "        -0.00422151,  0.00306441]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.data # data is a key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "13e448a8-9235-45d5-9d3f-03d844824766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([151.,  75., 141., 206., 135.,  97., 138.,  63., 110., 310., 101.,\n",
       "        69., 179., 185., 118., 171., 166., 144.,  97., 168.,  68.,  49.,\n",
       "        68., 245., 184., 202., 137.,  85., 131., 283., 129.,  59., 341.,\n",
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       "        42., 111.,  98., 164.,  48.,  96.,  90., 162., 150., 279.,  92.,\n",
       "        83., 128., 102., 302., 198.,  95.,  53., 134., 144., 232.,  81.,\n",
       "       104.,  59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n",
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       "       107.,  83., 103., 272.,  85., 280., 336., 281., 118., 317., 235.,\n",
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       "        78.,  93., 252., 150.,  77., 208.,  77., 108., 160.,  53., 220.,\n",
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       "        71.,  47., 187., 125.,  78.,  51., 258., 215., 303., 243.,  91.,\n",
       "       150., 310., 153., 346.,  63.,  89.,  50.,  39., 103., 308., 116.,\n",
       "       145.,  74.,  45., 115., 264.,  87., 202., 127., 182., 241.,  66.,\n",
       "        94., 283.,  64., 102., 200., 265.,  94., 230., 181., 156., 233.,\n",
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       "        31., 129.,  83., 275.,  65., 198., 236., 253., 124.,  44., 172.,\n",
       "       114., 142., 109., 180., 144., 163., 147.,  97., 220., 190., 109.,\n",
       "       191., 122., 230., 242., 248., 249., 192., 131., 237.,  78., 135.,\n",
       "       244., 199., 270., 164.,  72.,  96., 306.,  91., 214.,  95., 216.,\n",
       "       263., 178., 113., 200., 139., 139.,  88., 148.,  88., 243.,  71.,\n",
       "        77., 109., 272.,  60.,  54., 221.,  90., 311., 281., 182., 321.,\n",
       "        58., 262., 206., 233., 242., 123., 167.,  63., 197.,  71., 168.,\n",
       "       140., 217., 121., 235., 245.,  40.,  52., 104., 132.,  88.,  69.,\n",
       "       219.,  72., 201., 110.,  51., 277.,  63., 118.,  69., 273., 258.,\n",
       "        43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
       "       140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
       "        84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
       "        94., 183.,  66., 173.,  72.,  49.,  64.,  48., 178., 104., 132.,\n",
       "       220.,  57.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.target # target is a key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e8172c33-3a0b-4ad2-a514-edb51ec9b19d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.feature_names # Independent Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f329d1e0-ca61-4f55-87ce-a9fc5d875167",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(diabetes.data, columns = diabetes.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0649f5a6-77e1-4458-9f5b-fb4862e8bfd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
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       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068332</td>\n",
       "      <td>-0.092204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005670</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002861</td>\n",
       "      <td>-0.025930</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
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       "      <td>-0.031988</td>\n",
       "      <td>-0.046641</td>\n",
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       "      <td>-0.002566</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.031193</td>\n",
       "      <td>0.007207</td>\n",
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       "    <tr>\n",
       "      <th>438</th>\n",
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       "      <td>-0.015906</td>\n",
       "      <td>-0.067642</td>\n",
       "      <td>0.049341</td>\n",
       "      <td>0.079165</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>-0.018114</td>\n",
       "      <td>0.044485</td>\n",
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       "    <tr>\n",
       "      <th>439</th>\n",
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       "      <td>-0.015906</td>\n",
       "      <td>0.017293</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.013840</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>-0.011080</td>\n",
       "      <td>-0.046883</td>\n",
       "      <td>0.015491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.026560</td>\n",
       "      <td>0.044529</td>\n",
       "      <td>-0.025930</td>\n",
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       "    <tr>\n",
       "      <th>441</th>\n",
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       "      <td>-0.073030</td>\n",
       "      <td>-0.081413</td>\n",
       "      <td>0.083740</td>\n",
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       "      <td>0.173816</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.004222</td>\n",
       "      <td>0.003064</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 10 columns</p>\n",
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       "          age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
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       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
       "439  0.041708  0.050680 -0.015906  0.017293 -0.037344 -0.013840 -0.024993   \n",
       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081413  0.083740  0.027809  0.173816   \n",
       "\n",
       "           s4        s5        s6  \n",
       "0   -0.002592  0.019907 -0.017646  \n",
       "1   -0.039493 -0.068332 -0.092204  \n",
       "2   -0.002592  0.002861 -0.025930  \n",
       "3    0.034309  0.022688 -0.009362  \n",
       "4   -0.002592 -0.031988 -0.046641  \n",
       "..        ...       ...       ...  \n",
       "437 -0.002592  0.031193  0.007207  \n",
       "438  0.034309 -0.018114  0.044485  \n",
       "439 -0.011080 -0.046883  0.015491  \n",
       "440  0.026560  0.044529 -0.025930  \n",
       "441 -0.039493 -0.004222  0.003064  \n",
       "\n",
       "[442 rows x 10 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6df13452-0b0f-4f54-a03a-a69319200842",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['target'] = diabetes.target\n",
    "# Adding target values into array of target to data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bc97a461-e700-4837-992a-06ac411f622f",
   "metadata": {},
   "outputs": [
    {
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       "          age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
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       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
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       "441 -0.045472 -0.044642 -0.073030 -0.081413  0.083740  0.027809  0.173816   \n",
       "\n",
       "           s4        s5        s6  target  \n",
       "0   -0.002592  0.019907 -0.017646   151.0  \n",
       "1   -0.039493 -0.068332 -0.092204    75.0  \n",
       "2   -0.002592  0.002861 -0.025930   141.0  \n",
       "3    0.034309  0.022688 -0.009362   206.0  \n",
       "4   -0.002592 -0.031988 -0.046641   135.0  \n",
       "..        ...       ...       ...     ...  \n",
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       "438  0.034309 -0.018114  0.044485   104.0  \n",
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       "441 -0.039493 -0.004222  0.003064    57.0  \n",
       "\n",
       "[442 rows x 11 columns]"
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     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data\n",
    "# Read the data \n",
    "# Ends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "961198c0-05de-40b3-be9e-0029db4c5df5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. data preparation : EDA/Feature Engineering\n",
    "# Starts\n",
    "# This diabetes data set was already prepared/scaled no need of doing EDA/Feature Engineering\n",
    "# Ends\n",
    "\n",
    "# 3. Divide the data into X and y\n",
    "# Starts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "21b3e04f-e8ed-4300-a0ef-e99b1fabe21d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#simple linear regression : take only single variable or feature\n",
    "X = data[['bmi']] # column or feature\n",
    "y = data['target']\n",
    "# Divide the data into X and y\n",
    "# Ends\n",
    "\n",
    "# Mulitple linear regression means multiple variables or features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3dc90226-633f-44d5-8206-87df99c34189",
   "metadata": {},
   "outputs": [
    {
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       "<p>442 rows × 1 columns</p>\n",
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       "          bmi\n",
       "0    0.061696\n",
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       "3   -0.011595\n",
       "4   -0.036385\n",
       "..        ...\n",
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       "439 -0.015906\n",
       "440  0.039062\n",
       "441 -0.073030\n",
       "\n",
       "[442 rows x 1 columns]"
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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  {
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   "execution_count": 23,
   "id": "0bfe2984-542a-4cb8-a594-9b81954df58d",
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    {
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       "0      151.0\n",
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       "       ...  \n",
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       "439    132.0\n",
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   "cell_type": "code",
   "execution_count": 24,
   "id": "6b63eae1-d99f-44c8-9db5-47d61e7cc4aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Train-test Split\n",
    "# Starts\n",
    "\n",
    "# scikit-learn : \n",
    "# It is simple and efficient tools for predictive(ML built on Numpy,Scipy and Matplot lib) data analysis.\n",
    "# It provides the algorithmic approach\n",
    "# It provides the implementation.\n",
    "\n",
    "# It has 3 algorithms classification, Regression and clustering\n",
    "# classification and regression are important algorithms.\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "37c568d1-2097-402f-a68f-9be1ac7be95d",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
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       "      <th></th>\n",
       "      <th>bmi</th>\n",
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       "      <th>382</th>\n",
       "      <td>0.060618</td>\n",
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       "      <td>0.025051</td>\n",
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       "      <th>391</th>\n",
       "      <td>-0.069797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>-0.021295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>-0.012673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>-0.011595</td>\n",
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       "      <th>86</th>\n",
       "      <td>-0.075186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>0.003494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>-0.006206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>-0.013751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>353 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          bmi\n",
       "382  0.060618\n",
       "195  0.025051\n",
       "391 -0.069797\n",
       "117 -0.021295\n",
       "337 -0.012673\n",
       "..        ...\n",
       "275 -0.011595\n",
       "86  -0.075186\n",
       "212  0.003494\n",
       "364 -0.006206\n",
       "340 -0.013751\n",
       "\n",
       "[353 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train_test_split do that split arrays or matrices into random train and test subsets.\n",
    "# train_test_split is divided into X_train, X_test, y_train, y_test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 111)\n",
    "# Order is important X_train, X_test, y_train, y_test\n",
    "# test_size is 80:20\n",
    "\n",
    "# random_state = 111 means reproduce the same result that means how many time we exesute this cell\n",
    "# will give you the same result\n",
    "# Example: \n",
    "# 382 0.060618\n",
    "# the value 0.060618 remains same that is random_state\n",
    "# 111 we can give any value in random state\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a685965b-1902-4cac-a0e3-a70635e18c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "382    132.0\n",
       "195    122.0\n",
       "391     63.0\n",
       "117    281.0\n",
       "337     91.0\n",
       "       ...  \n",
       "275     94.0\n",
       "86      55.0\n",
       "212     73.0\n",
       "364    262.0\n",
       "340    216.0\n",
       "Name: target, Length: 353, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train\n",
    "\n",
    "# Train-test Split\n",
    "# Ends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5045917d-92f4-43e5-8d2c-d21135d798d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. scaling\n",
    "# scaling is already done on this diabetes data set.\n",
    "\n",
    "# 6. model training\n",
    "# Starts\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "cc24d9bd-a83d-43e7-afce-a58a6edc1088",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()\n",
    "# model is empty object is created for LinearRegression class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "194b5485-9fa7-4d6f-a635-99d9c8759de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0aaf90bd-3c16-4b6e-8f30-18f8719848b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)\n",
    "# trained the model on X_train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5db6375d-81d9-43df-aaa1-fe29b102b82b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([967.56370916])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_ # coef is m, beta1\n",
    "# model is the instance of class(LinearRegression)\n",
    "# Access coef variable in the class we use instance/object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1a0a3022-fcd7-475d-ac33-91ad683d5c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150.90412145452734"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_ # coef is c, betanot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b098341e-c287-4257-9c7b-494784c846c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "# predict the model on X_test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7e97d08f-da16-490d-8d26-7c0f1ba17bfd",
   "metadata": {},
   "outputs": [
    {
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       "      <th>57</th>\n",
       "      <td>-0.063330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>-0.033151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          bmi\n",
       "145  0.128521\n",
       "63  -0.037463\n",
       "193 -0.005128\n",
       "206  0.026128\n",
       "316  0.014272\n",
       "..        ...\n",
       "373 -0.010517\n",
       "292 -0.022373\n",
       "330  0.030440\n",
       "57  -0.063330\n",
       "338 -0.033151\n",
       "\n",
       "[89 rows x 1 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "eb7ccc0d-6550-4ef3-a7b8-efa0d70528cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([275.25594645, 114.65676186, 145.9423173 , 176.18502089,\n",
       "       164.71365056, 150.11372469, 157.41368763, 122.99957664,\n",
       "       155.32798393, 128.21383588, 180.35642828, 143.8566136 ,\n",
       "       240.84183547, 202.25631709, 133.42809512, 191.82779861,\n",
       "       131.34239143, 146.98516915, 194.95635416, 164.71365056,\n",
       "       134.47094697, 143.8566136 , 119.8710211 , 182.44213198,\n",
       "        88.58546566, 132.38524328,  86.49976196, 111.52820632,\n",
       "       173.05646535, 178.27072459, 199.12776155, 115.69961371,\n",
       "       127.17098404, 208.51342818, 133.42809512, 138.64235436,\n",
       "       121.9567248 , 120.91387295, 159.49939132, 145.9423173 ,\n",
       "       185.57068752, 217.89909481, 127.17098404, 144.89946545,\n",
       "       142.81376176, 109.44250262, 155.32798393,  82.32835457,\n",
       "       156.37083578, 164.71365056, 170.97076165, 148.028021  ,\n",
       "       229.37046514,  72.94268794, 183.48498383,  92.75687305,\n",
       "       115.69961371, 167.84220611,  99.01398414, 126.12813219,\n",
       "       104.22824338, 115.69961371, 168.88505796,  87.54261381,\n",
       "       272.12739091, 155.32798393,  76.07124349, 143.8566136 ,\n",
       "       210.59913187, 180.35642828, 208.51342818, 167.84220611,\n",
       "        63.55702131, 166.79935426, 193.91350231, 199.12776155,\n",
       "       106.31394708,  76.07124349, 142.81376176, 189.74209492,\n",
       "        93.7997249 , 142.81376176, 241.88468731, 120.91387295,\n",
       "       140.72805806, 129.25668773, 180.35642828,  89.62831751,\n",
       "       118.82816925])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c536c634-cf0a-46b5-97b7-9867971e5b1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How the model is performing?\n",
    "\n",
    "# visualised the result\n",
    "plt.scatter(X_test, y_test, color = 'black', label = \"Actual data\")\n",
    "plt.plot(X_test, y_pred, color = 'red', linewidth = 3, label = \"linear regression line\")\n",
    "plt.xlabel(\"BMI\")\n",
    "plt.ylabel(\"One year preogression-target\")\n",
    "plt.title(\"My first linear regression model\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f552d3b7-5184-47c8-94f2-8039f772654b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}